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Model: LumiOpen/Llama-Poro-2-8B-SFT Source: Original Platform
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---
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datasets:
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- LumiOpen/poro2-instruction-collection
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language:
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- fi
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- en
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license: llama3.3
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Poro 2 8B SFT Model Card
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> **Note for most users**: This is an intermediate checkpoint from our post-training pipeline. Most users should use [Poro 2 8B Instruct](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) instead, which includes an additional round of Direct Preference Optimization (DPO) for improved response quality and alignment. This SFT-only model is primarily intended for researchers interested in studying the effects of different post-training techniques.
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Poro 2 8B SFT is a supervised fine-tuned model created from the Poro 2 8B Base model. This model has been trained for instruction following and conversational AI applications in both Finnish and English, but has not undergone preference tuning. It represents the intermediate step before Direct Preference Optimization (DPO) in our post-training pipeline.
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Poro 2 was created in a collaboration between [AMD Silo AI](https://www.amd.com/en/solutions/ai/silo-ai.html), the [TurkuNLP group](https://turkunlp.org/) of the University of Turku, and [High Performance Language Technologies](https://hplt-project.org/) (HPLT). Training was conducted on the [LUMI supercomputer](https://www.lumi-supercomputer.eu/), using compute resources generously provided by [CSC](https://csc.fi/) - IT Center for Science, Finland.
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For more details on our training and data generation pipeline, check out our [Continued Pretraining Playbook](https://rocm.blogs.amd.com/artificial-intelligence/multilingual-continued-pretraining/README.html).
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## Poro 2 Model Family
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The Poro 2 model family includes both 8B and 70B models, and there are three different versions released of the Poro 2 models: a base model, a post-training SFT-only checkpoint, and the final instruct model which is the SFT model plus a round of DPO.
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| Model | Based on | Base Model | SFT | Instruct |
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| :---: | :------: | :--------: | :-: | :------- |
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| Poro 2 8B | Llama 3.1 8B | [Poro 2 8B Base](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-base) | [Poro 2 8B SFT](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-SFT) | [Poro 2 8B Instruct](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) |
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| Poro 2 70B | Llama 3.1 70B | [Poro 2 70B Base](https://huggingface.co/LumiOpen/Llama-Poro-2-70B-base) | [Poro 2 70B SFT](https://huggingface.co/LumiOpen/Llama-Poro-2-70B-SFT) | [Poro 2 70B Instruct](https://huggingface.co/LumiOpen/Llama-Poro-2-70B-Instruct) |
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_What does Poro mean?_ Poro is the Finnish word for Reindeer! 🦌 These animals are native to Finland and hold a significant and historical role in Finnish culture.
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## Model Overview
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Poro 2 8B SFT is based on the Llama 3.1 8B architecture and has been supervised fine-tuned for instruction following. The model supports both English and Finnish conversations but has not undergone preference tuning for response quality optimization.
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| Hyperparameter | Value |
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| :------------- | :----: |
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| n_parameters | 8.03B |
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| n_layers | 32 |
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| n_heads | 32 |
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| n_kv_heads | 8 |
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| d_model | 4096 |
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| vocab_size | 128256 |
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| max_sequence_length | 8192 |
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| base_model | Llama-3.1-8B |
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## Training Process
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### Continued Pretraining
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The base Poro 2 8B model was created through continued pretraining on 165B tokens of Finnish, English, code, and math data.
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### Supervised Fine-Tuning (SFT)
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This model represents the SFT phase of post-training, using 1.4M instruction-following examples in English and Finnish, including:
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- English and Finnish Tulu 3 prompts with Llama-3.3-70B-Instruct responses (1.35M samples)
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- Multi-turn conversations generated using the Magpie method (14K samples)
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- Top-rated conversations from OASST2 and Avoin Avustaja datasets (5K samples)
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- Translation samples from EuroParl (1K samples)
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We release the [Poro 2 instruction collection](https://huggingface.co/datasets/LumiOpen/poro2-instruction-collection).
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## SFT Hyperparameters
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| Hyperparameter | Value |
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| :------------: | :---: |
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| Epochs | 2 |
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| Global batch size | 64 |
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| Learning rate | 5e-6 |
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| LR scheduler | linear |
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| Warmup ratio | 0.03 |
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| Max sequence length | 4,096 |
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## Evaluation Results
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Poro 2 8B SFT shows substantial improvements in Finnish instruction-following capabilities compared to Llama 3.1 8B Instruct, while maintaining strong English performance. Note that the final Instruct model (with DPO) performs significantly better.
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### Finnish Instruction Following
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| | Poro 2 8B SFT | Llama 3.1 8B Instruct | Poro 2 8B Instruct |
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|----------------|------------------|------------------------|--------------------|
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| IFEval Finnish | 64.69 | 47.31 | **66.54** |
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| MTBench Finnish | 5.92 | 4.10 | **6.75** |
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| AlpacaEval 2 Finnish | 16.80 | 2.05 | **28.89** |
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### English Instruction Following
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| | Poro 2 8B SFT | Llama 3.1 8B Instruct | Poro 2 8B Instruct |
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|----------------|--------|------------------------|--------------------|
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| IFEval | **79.66** | 79.48 | 79.29 |
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| MTBench | 7.07 | **7.70** | 7.33 |
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| AlpacaEval 2 | 29.67 | 32.70 | **35.30** |
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### MTBench scores per-category
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| | Finnish | English |
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|-----------------|---------|-----------|
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| Coding | 4.55 | 5.30 |
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| Extraction | 6.20 | 8.15 |
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| Humanities | 7.30 | 8.25 |
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| Math | 4.60 | 7.10 |
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| Reasoning | 4.45 | 5.75 |
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| Roleplay | 6.55 | 7.25 |
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| STEM | 6.50 | 7.15 |
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| Writing | 7.24 | 7.65 |
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### MTBench scores per-turn
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| | Finnish | English |
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|-----------------|---------|-----------|
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| first turn | 6.76 | 7.90 |
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| second turn | 5.08 | 6.25 |
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**Overall**: ~16% average improvement in Finnish instruction-following benchmarks compared to Llama 3.1 8B Instruct, with maintained English performance. The additional DPO step in the Instruct model provides further improvements.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "LumiOpen/Poro-2-8B-SFT"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Finnish conversation example
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messages = [
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{"role": "user", "content": "Kerro minulle Suomen historiasta."}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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)
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outputs = model.generate(
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inputs,
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max_new_tokens=500,
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temperature=0.7,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Research Applications
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This SFT-only model is particularly useful for researchers studying:
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- The effects of supervised fine-tuning vs. preference tuning
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- Comparative analysis of different post-training techniques
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- Ablation studies on instruction-following capabilities
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- Cross-lingual transfer in instruction-following tasks
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- The impact of DPO on model behavior and alignment
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## Intended Use
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Poro 2 8B SFT is primarily intended for:
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- **Research purposes**: Studying post-training techniques and their effects
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- **Comparative analysis**: Understanding the contribution of different training phases
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- **Educational applications**: Learning about instruction-following model development
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- **Development**: As a starting point for further preference tuning experiments
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**For production use cases**, we recommend using [Poro 2 8B Instruct](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) instead.
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## Ethical Considerations and Limitations
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Poro 2 8B SFT is a research checkpoint optimized for English and Finnish instruction following. As this model has not undergone preference tuning, it may be more prone to generating responses that are misaligned with user expectations compared to the final Instruct model.
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Key limitations:
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- **No preference tuning**: May generate responses that are less aligned or of lower quality than the Instruct version
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- Limited proficiency in languages other than English and Finnish
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- May occasionally generate biased, inappropriate, or factually incorrect content
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- Performance may vary significantly for specialized or technical domains
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- Context window limited to 8,192 tokens
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- May struggle with very recent events (knowledge cutoff limitations)
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**Safety Considerations:**
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- This model should primarily be used for research purposes
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- Users should verify important factual claims independently
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- The model should not be used for medical, legal, or financial advice without human oversight
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- Responses should be reviewed for appropriateness in sensitive contexts
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- Consider using the Instruct version for better alignment and response quality
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## License
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Built with Llama
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Poro 2 8B SFT is released under the Llama 3.3 Community License. Please review the license terms before use.
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## Citation
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```bibtex
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@misc{poro2_2025,
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title={Poro 2: Continued Pretraining for Language Acquisition},
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author={Elaine Zosa and Jouni Louma and Kai Hakala and Antti Virtanen and Mika Koistinen and Risto Luukkonen and Akseli Reunamo and Sampo Pyysalo and Jonathan Burdge},
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year={2025},
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howpublished={LumiOpen}
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}
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```
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## Acknowledgments
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We thank CSC - IT Center for Science, Finland for providing access to the LUMI supercomputer. This work was supported by the High Performance Language Technologies (HPLT) project and conducted in collaboration with TurkuNLP from the University of Turku. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101070350.
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config.json
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||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
9
train_results.json
Normal file
9
train_results.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"epoch": 2.0,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
1417
trainer_state.json
Normal file
1417
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4f7ca7301e34abab61a8d77adcefb3448f3fab5a965a245e288750918137f0d2
|
||||
size 7288
|
||||
Reference in New Issue
Block a user